Radar target detection method and system under plasma sheath coating

Abstract

This invention discloses a radar target detection method and system under plasma sheath coverage, mainly addressing the problem of poor target detection capability in existing technologies for plasma sheath-covered target scenarios. The implementation scheme is as follows: The received pulse Doppler radar signal is obtained and pulse compression processing is performed; a sliding window is set for the compressed echo signal, and a first threshold is calculated to determine the signals within the sliding window, obtaining detection signals that pass the first threshold; the likelihood ratio function of the detection signal is obtained, and the logarithm of the likelihood ratio function is taken to obtain a weighted statistic; the detection signal is subjected to noise normalization processing, and the normalized value is substituted into the weighted statistic to obtain a detection statistic, thus obtaining a false alarm probability function; a second threshold is determined based on the false alarm probability function, and it is compared with the detection statistic to obtain the target detection result. This invention can reduce the impact of radar echo noise interference, improve the radar detection signal-to-noise ratio, and can be used for radar detection and parameter estimation of targets covered by plasma sheaths.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, and specifically relates to a radar target detection method and system, which can be used for radar detection and parameter estimation of hypersonic vehicles. Background Technology

[0002] Hypersonic vehicles are those that fly at speeds exceeding Mach 5, typically operating in the near-space region between 20 km and 100 km above the Earth's surface. When a hypersonic vehicle re-enters the atmosphere at extremely high speeds, its immense kinetic energy is converted into heat, creating a high-temperature stagnation zone at and around the vehicle's nose. Within this zone, air molecules undergo intense thermal ionization, forming a dense plasma sheath structure on the vehicle's surface.

[0003] The plasma sheath significantly impacts the radar detection process. On one hand, plasma, as an electromagnetic medium, alters the propagation characteristics of electromagnetic waves, causing amplitude attenuation and phase distortion in the radar signal during propagation. On the other hand, due to the plasma sheath's pronounced fluid properties, its scattering elements at different spatial locations correspond to different motion states, introducing different Doppler frequency components and creating complex Doppler spread effects in the radar echo. This effect introduces false targets or energy diffusion phenomena into the radar's one-dimensional range profile, leading to a decrease in the echo signal-to-noise ratio and a significant reduction in target detection and tracking performance.

[0004] Most existing radar target detection methods are based on the point target assumption. In environments with plasma sheath interference, their detection performance deteriorates significantly, making effective target detection difficult. To address this issue, some studies have proposed detection methods based on Doppler frequency compensation. These methods extract Doppler information from the target echo and compensate for the echo signal's frequency, thereby achieving energy accumulation and improving the detection signal-to-noise ratio (SNR). However, these methods typically rely on accurate estimation of the Doppler frequency. In low SNR or multi-target scenarios, the Doppler parameters are difficult to extract accurately, limiting the applicability of these methods.

[0005] Patent document CN202311372017.5 discloses "a radar target detection method under the influence of a plasma sheath." This method extracts target Doppler information and performs frequency alignment processing on the echo signal, ensuring the target body echo maintains a consistent position in the one-dimensional range profile. This concentrates the energy of the target body and the plasma sheath echo onto the same spectral peak, reducing the range-Doppler coupling effect and improving the target detection signal-to-noise ratio. However, in practice, this method cannot obtain the frequency of a reference target without considering the Doppler effect, and it is difficult to obtain accurate Doppler estimation results under low signal-to-noise ratio conditions, thus affecting the overall detection performance.

[0006] Furthermore, Ding Yi et al., in their paper "Research on Anomaly Suppression Method of Plasma-Sheath-Covered Reentry Target," proposed a Doppler extraction and interference signal filtering method. This method first extracts and compensates for the Doppler frequency of the radar echo, then designs a suppression filter to filter the compensated signal, thereby reducing interference. While this method improves detection performance to some extent, it still relies on accurate estimation of the Doppler frequency. When the echo signal-to-noise ratio is low, the Doppler parameters are difficult to extract reliably, and new interference components are introduced, thus limiting its application in complex environments.

[0007] Therefore, how to achieve stable and effective radar target detection in a low signal-to-noise ratio and complex multi-scattering environment under plasma sheath conditions remains a technical problem that urgently needs to be solved. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of the existing technology by proposing a radar target detection method and system under plasma sheath coverage, so as to reduce the influence of interference echoes, improve the radar target detection performance under plasma sheath coverage, and achieve robust target detection.

[0009] The technical approach to achieving the objective of this invention is as follows: by setting a sliding window, the signal within the window is detected at a low threshold to reduce the impact of noise interference echoes; then, by performing signal weighted fusion on the signals that have exceeded the threshold, the plasma echo signal, which was originally an interference signal, is transformed into a useful detection signal, thereby reducing the impact of plasma sheath interference echoes and improving the detection performance of radar targets under plasma sheath coverage.

[0010] To achieve the above objectives, the technical solution adopted by the present invention includes the following steps:

[0011] 1. A radar target detection method under a plasma sheath, characterized in that it comprises:

[0012] (1) Pulse Doppler radar receives the transmitted linear frequency modulated signal of a target in a plasma sheath-enclosed scenario. echo signal The pulse is then compressed to obtain the compressed echo signal. ;

[0013] (2) In the compressed echo signal Set up a sliding window to obtain local noise power. Based on noise power A first threshold is established, and signals within the sliding window are judged to obtain detection signals that have exceeded the first threshold. ;

[0014] (3) The detection signal Make a binary hypothesis and obtain its probability density function and likelihood ratio function. And take the logarithm of the likelihood ratio function to obtain the weighted statistic. ;

[0015] (4) The detection signal Perform noise normalization and substitute the normalization result into the weighted statistic. Obtain detection statistics Obtain its false alarm probability function ;

[0016] (5) Based on the false alarm probability function Determine the second threshold and compare it with the detection statistics. The results are compared to obtain the target detection results.

[0017] Furthermore, in the above, the detection signal in (3) Make a binary hypothesis and obtain its probability density function and likelihood ratio function. ,include:

[0018] 3a) The detection signal Make a binary hypothesis

[0019] 3b) Under the two opposing assumptions, for the first... There are echo points, assuming their noise signals follow a mean of 0 and a variance of . The target signal follows a complex Gaussian distribution with a mean of 0 and a variance of . The complex Gaussian distribution of is obtained; The first under the assumption Probability density of each echo point and in The first under the assumption Probability density of each echo point :

[0020] 3c) Assuming that each echo point in the detected signal is independent, we obtain... probability density of the detected signal under the assumption and in probability density of the detected signal under the assumption ;

[0021] 3d) will be probability density of the detected signal under the assumption With probability density of the detected signal under the assumption Dividing yields the likelihood ratio function ;

[0022] Furthermore, in step (3), the weighted statistic is obtained by taking the logarithm of the likelihood ratio function. ,include:

[0023] 3e) For the likelihood ratio function The detection value is obtained by taking the logarithm. :

[0024] 3f) For the detected value After simplification, we obtain the weighted statistics. :

[0025] 2. A radar target detection system under a plasma sheath, characterized in that it comprises:

[0026] The signal generation module is used to generate the linear frequency modulated signal transmitted by the pulse Doppler radar, the received signal, and the pulse-compressed echo signal in the scenario of a target covered by a plasma sheath.

[0027] The first threshold detection module is used to set a sliding window for the compressed echo signal, calculate the local noise power and the first threshold to make a judgment on the signal within the sliding window, and obtain the detection signal that has passed the first threshold.

[0028] The weighted statistics calculation module is used to calculate the likelihood ratio function of the detected signal and take the logarithm of the likelihood ratio function to calculate the weighted statistics.

[0029] The false alarm probability function calculation module is used to perform noise normalization processing on the detection signal, and substitute the normalization result into the weighted statistics to obtain the detection statistics and calculate its false alarm probability function.

[0030] The second threshold detection module is used to calculate the second threshold and compare it with the detection statistics to obtain the target detection result.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] Firstly, because the present invention fully considers the influence of the plasma sheathing scenario on the amplitude and phase modulation and Doppler modulation of the radar echo, the echo signal it acquires is more accurate, which is beneficial to the accurate detection of the signal.

[0033] Secondly, this invention uses a first threshold sliding window detection to perform preliminary detection of the echo signal, thereby reducing the impact of noise interference signals.

[0034] Third, by performing weighted fusion on the detection signal to obtain weighted statistics, this invention transforms the plasma echo signal, which was originally an interference, into a useful detection signal, reducing the influence of plasma sheath interference echo. This avoids the problem that existing Doppler compensation-based methods cannot achieve effective signal accumulation due to inaccurate Doppler frequency estimation at low signal-to-noise ratios, thus improving the radar detection signal-to-noise ratio. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the implementation of the radar target detection method under plasma sheath coverage according to the present invention.

[0036] Figure 2 Block diagram of the radar target detection system under plasma sheath of the present invention;

[0037] Figure 3 This is a simulation result diagram of the radar target detection method under plasma sheath coverage according to the present invention;

[0038] Figure 4 This is a simulation comparison diagram of the detection probability of the radar target detection method under plasma sheath coverage of the present invention and the existing radar target detection methods. Specific implementation methods

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should all fall within the protection scope of the present invention.

[0040] Example 1: Radar target detection method under plasma sheath.

[0041] Reference Figure 1 The implementation steps of this example include:

[0042] Step 1) Acquire the Doppler pulse radar received signal and perform pulse compression processing.

[0043] This step involves acquiring the received echo of the linear frequency modulated signal emitted by a pulse Doppler radar in a plasma sheath-enclosed target scenario. Compared to existing radar detection methods, it fully considers the effects of the plasma sheath-enclosed scenario on the amplitude and phase modulation and Doppler modulation of the radar echo. The received signal is then pulse-compressed to obtain the compressed echo data. The implementation steps include:

[0044] 1.1) Pulse Doppler radar transmits linear frequency modulated signals in scenarios where targets are encased in plasma sheaths. :

[0045] ;

[0046] in, Indicates a fast time. Represents a rectangular window function. , Indicates the pulse width. represents an imaginary number, Indicates the carrier frequency. Indicates frequency modulation;

[0047] 1.2) Based on the property that the transmitted linear frequency modulated signal is reflected by both the target and the plasma sheath, pulse Doppler radar acquires the received echo of the transmitted linear frequency modulated signal in a scenario where the target is encased in a plasma sheath. It is a composite echo of the target body and the plasma sheath, which can be represented as:

[0048] ;

[0049] Where I represents the number of target scattering points, Indicates the first Complex reflection coefficient at each scattering point Represents a delayed variable. Represents the speed of light. This indicates the instantaneous distance between the target and the radar. For the first Doppler frequencies at each scattering point This represents zero-mean complex Gaussian white noise;

[0050] 1.3) Regarding the received echo signal Pulse compression is performed to obtain the compressed echo signal. :

[0051] ,

[0052] in, B represents the amplitude and phase modulation of the compressed echo signal, where B is the signal bandwidth.

[0053] From the echo signal As can be seen from the formula, due to the influence of the target being covered by the plasma sheath, the radar echo is not a traditional point target, but exhibits extended target characteristics. The target echoes interfere with each other. In order to effectively detect the target, it is necessary to perform a first threshold detection on the echo signal to obtain the detection signal, and then perform weighted fusion on the detection signal to remove the influence of interference.

[0054] Step 2) Perform a first threshold detection on the compressed echo signal to obtain the detection signal.

[0055] In response to the presence of noise interference, this step involves processing the echo signal. Set up a sliding window, designate the distance unit corresponding to the signal within the window as the detection unit, and then calculate the corresponding local noise power. To obtain the first threshold The echo signal is detected, and noise is filtered out by a first threshold to obtain a useful detection signal. The implementation steps include:

[0056] 2.1) Regarding the echo signal Perform discrete sampling to obtain the sampled echo signal. :

[0057] ,

[0058] in, Indicates the echo signal No. The sampling result, M represents the number of discrete sampling points, and each sampling result corresponds to a distance unit;

[0059] 2.2) The sampled echo signal Set a sliding window with a length of [value missing]. Set the distance unit corresponding to the signal inside the window as the detection unit;

[0060] 2.3) Select E distance units on both sides of the detection unit as reference units, and obtain the local noise power of the signal within the reference units. :

[0061] ,

[0062] in, This represents the echo signal corresponding to the reference unit on both sides of the detection unit;

[0063] 2.4) Based on noise power Forming the first threshold :

[0064] Where T is the standardization factor;

[0065] 2.5) Compare each echo signal within the sliding window with the first threshold. Compare and make a judgment:

[0066] If the echo signal is greater than the first threshold Then set it as the detection signal. :

[0067] ,

[0068] in, Indicates the first A number greater than the first threshold echo signal, The number of echo signals passing through the first threshold within the window;

[0069] Otherwise, the corresponding echo signal will be discarded.

[0070] Step 3) Calculate the weighted statistics of the detected signal.

[0071] This step first makes a binary hypothesis about the detected signal, then calculates the probability density function of the detected signal under the two opposing hypotheses, then constructs the likelihood ratio function according to the NP criterion, and simplifies and optimizes it by taking the logarithm to obtain the weighted statistics of the detected signal. The implementation steps include:

[0072] 3.1) The detection signal Make a binary hypothesis:

[0073] ,

[0074] in Indicates noise signal, Indicates the target signal. This indicates that there is no target hypothesis. This indicates the existence of a target hypothesis;

[0075] 3.2) Under the two opposing assumptions, for the first... There are echo points, assuming their noise signals follow a mean of 0 and a variance of . The target signal follows a complex Gaussian distribution with a mean of 0 and a variance of . The complex Gaussian distribution of is obtained; The first under the assumption Probability density of each echo point and The first under the assumption Probability density of each echo point They are respectively:

[0076] ,

[0077] 3.3) Based on this, assuming that each echo point in the detected signal is independent, we can obtain... probability density of the detected signal under the assumption and probability density of the detected signal under the assumption They are respectively:

[0078] ,

[0079] 3.4) Based on the detection signal in Assume and The probability density under the assumptions yields the likelihood ratio function under the NP criterion. :

[0080] ;

[0081] 3.5) For the likelihood ratio function Take the logarithm to obtain the corresponding detection value. :

[0082] ,

[0083] in, and These are respectively represented as the detection signal number 1 echo points exist The probability density under the assumptions and The probability density under the assumptions and These represent the detection signals respectively. The variance of the noise signal and the variance of the target signal in each echo point;

[0084] 3.6) Let For the detected value Remove constant terms Then, the weighted statistics are obtained by simplifying them. :

[0085] .

[0086] Step 4) Calculate the false alarm probability function of the detected signal.

[0087] Since noise is randomly distributed in radar target detection, high-energy noise can be mistakenly detected as a target. Therefore, a certain false alarm probability needs to be set to reduce its impact on radar target detection. To obtain the false alarm probability function of the detection signal, this step first normalizes the noise of the detection signal. The normalization result is then substituted into the weighted statistics to obtain the detection statistics, which are assumed to follow a weighted chi-square distribution to obtain the corresponding cumulative distribution function. The false alarm probability function is then calculated, and the implementation steps include:

[0088] 4.1) For the detected signal, the first... The echo points are subjected to noise normalization processing to obtain the normalized result. :

[0089] ;

[0090] 4.2) Substitute the normalized result into the weighted statistic to replace the values ​​in the original text. , and obtain the detection statistics :

[0091] ;

[0092] 4.3) Set the detection statistic Obeying the degree of freedom Let the weight vector be Weighted chi-square distribution ,in, , and These represent the detection signals respectively. No. The variance of the noise signal and the variance of the target signal in each echo point;

[0093] 4.4) Based on the detection statistics and weighted chi-square distribution Obtain the corresponding cumulative distribution function :

[0094] ,

[0095] in, It is the detection threshold;

[0096] 4.5) Based on the cumulative distribution function Obtain the probability of false alarms :

[0097] .

[0098] Step 5) Calculate the second threshold and perform the final target detection.

[0099] Since the first threshold detection only filters out most of the noise interference and cannot completely eliminate it, in order to obtain a more accurate target detection result, this step sets a second threshold for re-detection to obtain a more accurate determination of the target's presence or absence. The implementation steps include:

[0100] 5.1) Based on the false alarm probability function Obtain information about the detection threshold Functions:

[0101] ,

[0102] in, It is the cumulative distribution function inverse function, It represents the cumulative distribution function that follows a weighted chi-square distribution with k degrees of freedom and p weight vector;

[0103] 5.2) Based on the preset false alarm probability Obtain the second threshold :

[0104] ;

[0105] 5.3) Set the second threshold With detection statistics Comparison:

[0106] like If a target is detected,

[0107] like If no target is detected, then no target is detected.

[0108] It should be noted that the step numbers in the specification and claims of this invention are only for the purpose of clearly describing the embodiments of this invention and facilitating understanding, and their order is not limited.

[0109] Example 2: Radar target detection system under plasma sheath.

[0110] Reference Figure 2 This embodiment includes: a signal generation module 1, a first threshold detection module 2, a weighted statistics calculation module 3, a false alarm probability function calculation module 4, and a second threshold detection module 5. The first threshold detection module 2 includes: a window setting submodule 21, a noise power calculation submodule 22, a first threshold calculation submodule 23, and a detection decision submodule 24.

[0111] The working principle of the entire system is as follows:

[0112] The signal generation module 1 is used to generate a linear frequency modulated signal and a received signal transmitted by the pulse Doppler radar in the scenario of a target covered by a plasma sheath, and to perform pulse compression on the received signal to obtain a compressed echo signal, which is then transmitted to the first threshold detection module 2.

[0113] The first threshold detection module 2 sets a sliding window for the compressed echo signal transmitted by the received signal generation module 1, calculates the local noise power and a first threshold, and makes a decision on the signal within the sliding window. Specifically, the window setting submodule 21 sets a sliding window for the compressed echo signal, selects the corresponding echo data, and transmits the location of the sliding window to the noise power calculation submodule 22. The noise power calculation submodule 22 selects echoes near the sliding window as reference units based on the location of the sliding window, calculates the local noise power of the signal within the reference unit, and transmits it to the first threshold calculation submodule 23. The first threshold calculation submodule 23 sets a nominal factor and calculates the first threshold based on the local noise power, transmitting the first threshold to the detection decision submodule 24. The detection decision submodule 24 makes a decision on the signal within the sliding window based on the first threshold, obtains the detection signal that has exceeded the first threshold, and transmits it to the weighted statistics calculation module 3 and the false alarm probability function calculation module 4, respectively.

[0114] The weighted statistics calculation module 3 is used to make a binary hypothesis on the received detection signal, calculate the probability density function of the detection function and the likelihood ratio function of the detection signal, and take the logarithm of the likelihood ratio function to calculate the weighted statistics, which are then transmitted to the false alarm probability function calculation module 4.

[0115] The false alarm probability function calculation module 4 is used to perform noise normalization processing on the detection signal, substitute the normalization result into the weighted statistics to obtain the detection statistics, set the weighted chi-square distribution parameters that the detection statistics follow, further calculate its corresponding cumulative distribution function, and then calculate the false alarm probability function of the detection statistics. The detection statistics and the false alarm probability function are then transmitted to the second threshold detection module 5.

[0116] The second threshold detection module 5 is used to calculate the detection threshold function based on the received false alarm probability function, set the false alarm probability to calculate the corresponding second threshold, and then compare it with the detection statistics to obtain the target detection result for output.

[0117] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as program instruction products. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0118] In this embodiment, the direct coupling or communication connection between the modules can be achieved through indirect coupling or communication connection via interfaces, devices, or modules. The functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0119] The effectiveness of this invention can be further illustrated by the following simulation results:

[0120] I. Simulation Conditions

[0121] Hardware environment: CPU is Intel(R) Core(TM) i7-10700F with a clock speed of 2.90GHz, memory is 16.0GB, and operating system is 64-bit.

[0122] The software environment is MATLAB 2022b simulation software.

[0123] The target is set to always be within the radar's line-of-sight range, be illuminating by the Doppler pulse radar, and be encased in a plasma sheath. The target's altitude is set at 30 km, its speed at 25 Mach, and it is flying towards the radar. The carrier frequency of the Doppler pulse radar's transmitted linear frequency modulated signal is set to 5.9 GHz, the pulse width to 50 μs, the bandwidth to 5 MHz, the radar sampling frequency to 10 MHz, and the false alarm probability to 10%. -8 .

[0124] II. Simulation Content

[0125] Simulation 1: Under the above simulation conditions, with a fixed signal-to-noise ratio of 10dB, the present invention was used to detect a target covered by a plasma sheath. The results are as follows: Figure 3 .in:

[0126] Figure 3 (a) shows the pulse compression result of the radar received signal. As can be seen from this figure, the echo signal is composed of multiple scattering points, which is a complex echo.

[0127] Figure 3 (b) This figure shows the first threshold detection result corresponding to the sliding window. It can be seen from this figure that there are 3 echoes inside the window.

[0128] Passed the first threshold;

[0129] Figure 3 (c) Calculate the detection statistic and the second threshold for the detection signal that exceeds the threshold. This can be seen from the figure.

[0130] If the detection statistic of the echo is greater than the second threshold, the target is detected.

[0131] Simulation results show that the present invention effectively achieves target detection by fusing the body signal and the plasma sheath signal.

[0132] Simulation 2: Under the above simulation conditions, the signal-to-noise ratio (SNR) was set to vary from 4dB to 18dB, with a step size of 1dB and 1000 Monte Carlo runs. The present invention and four existing target detection methods were used to detect radar targets under different echo SNRs. The detection probability curves were obtained through Monte Carlo experiments, and the performance of different methods was compared. The results are as follows: Figure 4 .

[0133] From such Figure 4 As can be seen, due to the presence of a plasma sheath and the low signal-to-noise ratio, the advantages and disadvantages of various detection methods are analyzed as follows:

[0134] The existing point-target-based CA-CFAR method (CA-CFAR) has poor anti-interference performance, is most affected by the presence of interference signals, and cannot detect targets under low signal-to-noise ratio conditions.

[0135] While existing target detection methods based on Doppler compensation can achieve signal superposition and effectively distinguish between the body and the sheath, they push the interference signal near the target echo, leading to increased interference and poor performance at low signal-to-noise ratios.

[0136] Existing target detection methods based on edge detection utilize gradient information to detect targets. Although they can mitigate the effects of plasma sheathing to some extent, their performance is unsatisfactory under low signal-to-noise ratio conditions.

[0137] Existing target detection methods based on Doppler compensation perform better in comparison, but their performance depends on the extraction of Doppler information. Under low signal-to-noise ratio conditions, Doppler information may be extracted incorrectly or not at all.

[0138] This invention effectively filters out noise through a first threshold detection and achieves optimal detection results through signal fusion.

[0139] Simulation results show that the present invention reduces the impact of in-window noise by setting a first threshold, and uses the plasma echo signal, which was originally interference, to improve radar detection performance by using signal weighted fusion. This effectively solves the problems of plasma echo interference and reduced target echo signal-to-noise ratio, improves the detection signal-to-noise ratio, and increases the radar detection range. Its detection performance is superior to existing target detection methods.

Claims

1. A method for radar target detection under plasma sheath covering, characterized in that, include: (1) Pulse Doppler radar receives the transmitted linear frequency modulated signal of a target in a plasma sheath-enclosed scenario. echo signal The pulse is then compressed to obtain the compressed echo signal. ; (2) in the compressed echo signal The sliding window is set in the compressed echo signal to obtain the local noise power The first threshold is formed according to the noise power The detection signal exceeding the first threshold is obtained by judging the signal in the sliding window ; (3) performing binary hypothesis on the detection signal to obtain a probability density function and a likelihood ratio function , and taking logarithm of the likelihood ratio function to obtain a weighted statistic ; (4) performing noise normalization processing on the detection signal , substituting the normalized result into a weighted statistical quantity to obtain a detection statistical quantity , obtaining a false alarm probability function ; (5) According to the false alarm probability function determines the second threshold and compares it with the detection statistic to obtain the target detection result.

2. The method according to claim 1, characterized in that: The (1) pulse Doppler radar transmits a linear frequency modulation signal And the echo signal received by it in the scenario of a plasma sheath coated target And to the compressed echo signal Respectively as follows: ， ， ， wherein, denotes fast time, denotes a rectangular window function, , denotes pulse width, denotes imaginary, denotes carrier frequency, denotes frequency modulation rate, I denotes target scatterer number, denotes the complex reflection coefficient of the th scatterer, denotes delay variable, denotes light speed, denotes the instantaneous range of the target from the radar, is the Doppler frequency of the th scatterer, denotes zero-mean complex Gaussian white noise, is the amplitude and phase modulation of the compressed echo signal, and B is the signal bandwidth.

3. The method according to claim 1, characterized in that, The (2) in the compressed echo signal A sliding window is set in the local noise power , comprising: 2a) for the echo signal discretely sampled, obtaining a sampled echo signal : ， wherein, represents the echo signal The first sampled result, M represents the number of discrete sampling points, and each sampling result corresponds to a distance unit; 2b) on the sampled echo signal A sliding window is set with a window length of and the distance cells corresponding to the signals within the window are set as detection cells; 2c) Selecting E distance units on both sides of the detection unit as reference units, and obtaining the local noise power of the signals in the reference units : ， wherein represents the echo signal corresponding to the reference unit on both sides of the detection unit.

4. The method according to claim 1, characterized in that, In the (2), according to the noise power Forming a first threshold, judging the echo signal in the sliding window, comprising: 2d) in dependence of the noise power the first threshold is represented as: where T is a nominalization factor; 2e) comparing each echo signal within the sliding window with a first threshold comparison is made to decide: if the echo signal is greater than a first threshold then set it as a detection signal : ， wherein, represents the number of echo signals greater than a first threshold, is the number of echo signals within the window that pass the first threshold.​​ Otherwise, the corresponding echo signal will be discarded.

5. The method according to claim 1, characterized in that, The detection signal in (3) Make a binary hypothesis and obtain its probability density function and likelihood ratio function. ,include: 3a) detecting the signal The binary hypothesis is made that: ， wherein denotes a noise signal, denotes a target signal, denotes a no target hypothesis, denotes a target present hypothesis; 3b) Under the two opposing assumptions, for the first... There are echo points, assuming their noise signals follow a mean of 0 and a variance of . The target signal follows a complex Gaussian distribution with a mean of 0 and a variance of . The complex Gaussian distribution; then in The first under the assumption Probability density of each echo point and The first under the assumption Probability density of each echo point They are represented as follows: ； 3c) Assuming that each echo point in the detected signal is independent, then in probability density of the detected signal under the assumption and probability density of the detected signal under the assumption They are represented as follows: ； 3d) Obtain the likelihood ratio function based on the result of 3c). : 。 6. The method according to claim 1, characterized in that, In (3), the weighted statistic is obtained by taking the logarithm of the likelihood ratio function. ,include: 3e) For the likelihood ratio function The detection value is obtained by taking the logarithm. : ， in, Represented by natural numbers Logarithmic function with base 0. and These are respectively represented as the detection signal number 1 echo points exist The probability density under the assumptions and The probability density under the assumptions and They represent the detection signals respectively. The variance of the noise signal and the variance of the target signal in each echo point; 3f) Let For the detected value After simplification and removal of the constant term, the weighted statistic is obtained. : 。 7. The method according to claim 1, characterized in that, The detection signal in (4) Perform noise normalization and substitute the normalization result into the weighted statistic. Obtain detection statistics ,include: (4a) For the detected signal, the first The echo points are subjected to noise normalization processing to obtain the normalized result. : ； (4b) Substitute the normalized result into the weighted statistic to replace the value. , and obtain the detection statistics : 。 8. The method according to claim 1, characterized in that, The detection statistics are obtained in (4). False alarm probability function ,include: 4c) Hypothesis Detection Statistic Obeying the degree of freedom The weight vector is Weighted chi-square distribution , in, , and These represent the detection signals respectively. No. The variance of the noise signal and the variance of the target signal in each echo point; 4d) Based on the detection statistics and weighted chi-square distribution Obtain the corresponding cumulative distribution function : ， in, It is the detection threshold; 4e) Based on the cumulative distribution function Obtain the probability of false alarms : 。 9. The method according to claim 1, characterized in that, In (5), based on the false alarm probability function Determine the second threshold and compare it with the detection statistics. The comparison yields the target detection results, including: (5a) Based on the false alarm probability function Obtain information about the detection threshold Functions: ， in, It is the cumulative distribution function inverse function, It represents the cumulative distribution function that follows a weighted chi-square distribution with k degrees of freedom and p weight vector; (5b) Based on the preset false alarm probability Obtain the second threshold : ； (5c) Set the second threshold With detection statistics Comparison: like This indicates that a target has been detected. like If , it means that no target was detected.

10. A radar target detection system under a plasma sheath, characterized in that, include: The signal generation module is used to generate the linear frequency modulated signal transmitted by the pulse Doppler radar, the received signal, and the pulse-compressed echo signal in the scenario of a target covered by a plasma sheath. The first threshold detection module is used to set a sliding window for the compressed echo signal, calculate the local noise power and the first threshold to make a judgment on the signal within the sliding window, and obtain the detection signal that has passed the first threshold. The weighted statistics calculation module is used to calculate the likelihood ratio function of the detected signal and take the logarithm of the likelihood ratio function to calculate the weighted statistics. The false alarm probability function calculation module is used to perform noise normalization processing on the detection signal, and substitute the normalization result into the weighted statistics to obtain the detection statistics and calculate its false alarm probability function. The second threshold detection module is used to calculate the second threshold and compare it with the detection statistics to obtain the target detection result.

11. The system according to claim 10, characterized in that, The first threshold detection module includes: The window settings submodule is used to set a sliding window for the compressed echo signal and select the corresponding echo data. The noise power calculation submodule is used to calculate local noise power; The first threshold calculation submodule is used to set the nominal factor and calculate the first threshold value; The detection and decision submodule is used to make decisions on the signals within the sliding window and obtain the detection signals that have passed the first threshold.

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